AlgoEvo: Self-Evolving Agentic Search for Automated Algorithm Discovery· AlgoEvo：自我进化的代理搜索自动化算法发现
Large language models have advanced automated algorithm discovery by synthesizing executable code, but existing frameworks trap them in rigid search pipelines with pre-defined control flows. This limitation restricts adaptive reasoning, blocks cross-paradigm transfer, and discards valuable execution feedback. We propose AlgoEvo, a unified agentic framework that transforms automated algorithm discovery into an interactive, knowledge-accumulating process. An autonomous agent dynamically inspects, diagnoses, and edits code based on runtime feedback. A design skill hub decouples paradigm-specific knowledge from the core discovery engine, allowing a single workflow to seamlessly handle single-objective, multi-objective, and multi-component design. Meanwhile, a hierarchical experience mechanism
将自动化算法发现转变为互动式学习过程，通过代理实时反馈优化代码。
- 核心方法
- 提出AlgoEvo框架，通过自主代理根据运行时反馈动态检查、诊断和编辑代码，以及设计技能中心和分层经验机制来解耦范式特定知识和核心发现引擎。
- 适合谁读
- 适合研究者和工程师阅读
- 要解决的问题
- 现有的自动化算法发现框架由于预定义的控制流程限制了自适应推理、跨范式迁移和有价值的执行反馈。
- 关键实验
- 未提供
- 主要贡献
- 实现了单一流程中无缝处理单目标、多目标和多组件设计问题，增强了算法发现的适应性和互动性。
- 意义与局限
- 显著提高了自动化算法发现的灵活性和效率，但可能需要更多的计算资源和复杂的系统设计。